Method of stitching images captured by a vehicle, and a system thereof

ABSTRACT

The present disclosure relates to a method of stitching images captured by a vehicle. A first image and a second image are received. The first image and the second image are segmented based on characteristics of pixels. Groups of pixels having similar characteristics are identified to form clusters in a predetermined portion of overlap of the first image and the second image. A confidence score is generated for the first image and the second image. A difference in the confidence score is computed. At least one of, the first image capturing unit and the second image capturing unit is aligned to capture at least one of, a first aligned image and a second aligned image based on the difference in the confidence score. The first aligned image and the second aligned image are stitched.

TECHNICAL FIELD

The present disclosure relates to image processing. More particularly,the present disclosure relates to a method and a system for stitchingimages captured by a vehicle.

BACKGROUND

Current generation vehicles are driven autonomously, and autonomousvehicles need a 360° field of view to detect objects in variousdirections. The 360° field of view helps to prevent accidents, easyparking and ensures trouble-free driving. Typically, a plurality ofsensors placed on autonomous vehicle captures images individually andthe images captured are stitched together to produce an image providingthe 360° view around the autonomous vehicle. Stitching of images is aprocess of combining multiple disconnected captured images withoverlapping fields of view to produce a panoramic image. The panoramicimage provides the 360° field of view.

Traditional techniques for stitching the images uses high overlappingfields of view. The high overlapping fields of view is used to recognisethe disconnected images to form a single 360° image. Typically, threecameras are provided on each side of the vehicle to capture a field ofview of 180°. The three cameras are provided on each side, such that aneffective field of view of the three cameras capture the field of viewof 180°. However, in the traditional approach, as the amount of overlaprequired is high to stitch the images captured by the three cameras, thefield of view of each of the three cameras also overlap to a greatextent. Hence, a greater number of cameras are required to capture thefield of view of 180°. For example, in traditional systems, a total of12 cameras are used to capture 360° view. As autonomous vehicles run onbatteries, the amount of hardware has to be reduced to consume lesspower. Hence there is a need to provide a solution having less hardwareand captures the 360° field of view around the autonomous vehicle. Thehigh overlapping fields of view increases the processing of image data

The information disclosed in this background of the disclosure sectionis only for enhancement of understanding of the general background ofthe invention and should not be taken as an acknowledgement or any formof suggestion that this information forms the prior art already known toa person skilled in the art.

SUMMARY

In an embodiment, the present disclosure discloses a method of stitchingimages captured by a vehicle. The method comprises receiving, by anElectronic Control Unit (ECU) of the vehicle, a first image comprising afirst portion of a scene, from a first image capturing unit installed inthe vehicle and a second image comprising a second portion of the scene,from a second image capturing unit installed in the vehicle. Further,the method comprises segmenting, by the ECU, the first image and thesecond image based on one or more characteristics of a plurality ofpixels of the first image and the second image. Further, the methodcomprises identifying, by the ECU, one or more groups of pixels from theplurality of pixels in each of the first image and the second imagehaving similar characteristics from the one or more characteristics. Theidentified one or more groups of pixels form one or more clusters and acentroid is determined for each of the one or more clusters in apredetermined portion of overlap. Further, the method comprises,generating, by the ECU, a confidence score for the first image and thesecond image based on the centroid of each of the one or more clusters.Furthermore, the method comprises computing, by the ECU, a difference inthe confidence score of the first image and the second image. Moreover,the method comprises aligning, by the ECU, at least one of, the firstimage capturing unit and the second image capturing unit based on thedifference in the confidence score. At least one of, the aligned firstimage capturing unit and the aligned second image capturing unitcaptures at least one of, a first aligned image and a second alignedimage respectively. Thereafter, the method comprises stitching, by theECU, the first aligned image and the second aligned image.

In an embodiment, the present disclosure discloses an Electronic ControlUnit (ECU) for stitching images captured by a vehicle. The ECU comprisesa processor and a memory. The processor is configured to receive a firstimage comprising a first portion of a scene, from a first imagecapturing unit installed in the vehicle and a second image comprising asecond portion of the scene, from a second image capturing unitinstalled in the vehicle. Further, the processor is configured tosegment the first image and the second image based on one or morecharacteristics of a plurality of pixels of the first image and thesecond image. Further, the processor is configured to identify one ormore groups of pixels from the plurality of pixels in each of the firstimage and the second image having similar characteristics from the oneor more characteristic. The identified one or more groups of pixels formone or more clusters, wherein a centroid is determined for each of theone or more clusters a predetermined portion of overlap. Further, theprocessor is configured to generate a confidence score for the firstimage and the second image based on the centroid of each of the one ormore clusters. Furthermore, the processor is configured to compute adifference in the confidence score of the first image and the secondimage. Moreover, the processor is configured to align the first imagecapturing unit and the second image capturing unit based on thedifference in the confidence score. At least one of, the aligned firstimage capturing unit and the aligned second image capturing unitcaptures at least one of, a first aligned image and a second alignedimage respectively. Thereafter, the processor is configured to stitchthe first aligned image and the second aligned image.

In an embodiment, the present disclosure discloses a non-transitorycomputer readable medium including instructions stored thereon that whenprocessed by at least one processor cause an Electronic Control Unit(ECU) for stitching images captured by a vehicle. The ECU comprises aprocessor and a memory. The instructions causes the ECU to receive afirst image comprising a first portion of a scene, from a first imagecapturing unit installed in the vehicle and a second image comprising asecond portion of the scene, from a second image capturing unitinstalled in the vehicle. Further, the instructions cause the ECU tosegment the first image and the second image based on one or morecharacteristics of a plurality of pixels of the first image and thesecond image. Further, the instructions cause the ECU to identify one ormore groups of pixels from the plurality of pixels in each of the firstimage and the second image having similar characteristics from the oneor more characteristic. The identified one or more groups of pixels formone or more clusters, wherein a centroid is determined for each of theone or more clusters a predetermined portion of overlap. Further, theinstructions cause the ECU to generate a confidence score for the firstimage and the second image based on the centroid of each of the one ormore clusters. Furthermore, the instructions cause the ECU to compute adifference in the confidence score of the first image and the secondimage. Moreover, the instructions cause the ECU to align the first imagecapturing unit and the second image capturing unit based on thedifference in the confidence score. At least one of, the aligned firstimage capturing unit and the aligned second image capturing unitcaptures at least one of, a first aligned image and a second alignedimage respectively. Thereafter, the instructions cause the ECU to stitchthe first aligned image and the second aligned image.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The novel features and characteristics of the disclosure are set forthin the appended claims. The disclosure itself, however, as well as apreferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying figures. One or more embodiments are now described, by wayof example only, with reference to the accompanying figures wherein likereference numerals represent like elements and in which:

FIG. 1A shows an exemplary illustration showing a vehicle capturingimages, in accordance with some embodiments of the present disclosure;

FIG. 1B shows a first image and a second image captured by a vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 2 shows an internal architecture of an ECU for stitching imagescaptured by a vehicle, in accordance with some embodiments of thepresent disclosure;

FIG. 3 shows an exemplary flow chart illustrating method steps forstitching of images captured by a vehicle, in accordance with someembodiments of the present disclosure;

FIG. 4A-4E are exemplary illustrations of images during the process ofstitching, in accordance with some embodiments of the presentdisclosure;

FIG. 5 shows a block diagram of a general-purpose computer system forstitching of images captured by a vehicle, in accordance withembodiments of the present disclosure.

It should be appreciated by those skilled in the art that any blockdiagram herein represents conceptual views of illustrative systemsembodying the principles of the present subject matter. Similarly, itwill be appreciated that any flow charts, flow diagrams, statetransition diagrams, pseudo code, and the like represent variousprocesses which may be substantially represented in computer readablemedium and executed by a computer or processor, whether or not suchcomputer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word “exemplary” is used herein to mean“serving as an example, instance, or illustration.” Any embodiment orimplementation of the present subject matter described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiment thereof has been shown by way ofexample in the drawings and will be described in detail below. It shouldbe understood, however that it is not intended to limit the disclosureto the particular forms disclosed, but on the contrary, the disclosureis to cover all modifications, equivalents, and alternatives fallingwithin the scope of the disclosure.

The terms “comprises”, “comprising”, or any other variations thereof,are intended to cover a non-exclusive inclusion, such that a setup,device or method that comprises a list of components or steps does notinclude only those components or steps but may include other componentsor steps not expressly listed or inherent to such setup or device ormethod. In other words, one or more elements in a system or apparatusproceeded by “comprises . . . a” does not, without more constraints,preclude the existence of other elements or additional elements in thesystem or apparatus.

Embodiments of the present disclosure relate to stitching imagescaptured by a first capturing unit and a second capturing unit of avehicle. A first image and a second image are captured respectively bythe first capturing unit and the second capturing unit. A small amountof overlap may be present in the first image and second image. Further,the first image and the second image are segmented based oncharacteristics of pixels in respective images and clusters aredetermined based on the segments. Thereafter, a centroid is identifiedfor each cluster and confidence score is determined using the centroidsfor each image. When the difference between the confidence score is lessthan a threshold value, the images are considered to be aligned and arestitched together. When difference is above the threshold value, theimages are considered to be misaligned and cameras are alignedaccordingly to correct the misalignment. Further, the images capturedafter aligning the cameras are stitched together. Therefore, images arestitched using less number of cameras compared to conventional systems.

FIG. 1A shows an exemplary scene (100). The scene (100) may be a view ofimage capturing units installed on a vehicle (101). For example, forimage capturing units installed on a front side of the vehicle (101),the scene (100) may be the view ahead of the vehicle (101). Likewise,for image capturing units installed on a right side of the vehicle(101), the scene (100) may be the view to the right of the vehicle(101). In an embodiment, the scene (100) may comprise various objectslike tree (108), building (109), and other vehicles (car 110). Thevehicle (101) may be a car, a truck, a bus, and the like. Further, thevehicle (101) may be an autonomous vehicle or a self-driving vehicle.The vehicle (101) may comprise a first image capturing unit (102), asecond image capturing unit (103), and an Electronic Control Unit (ECU)(104). The present disclosure has been described considering only twoimage capturing units. This should not be considered as a limitation andthe present disclosure is applicable for a plurality of image capturingunits as well. The first image capturing unit (102) and the second imagecapturing unit (103) may be placed in the front side of the vehicle(101). Further, a pair of image capturing units may be placed each onrear side of the vehicle (101), right side of the vehicle (101) and theleft side of the vehicle (101) to capture images of the scene (100)around the vehicle (101) (image capturing units installed on right, leftand rear side of the vehicle (101) are not shown in FIG. 1 and areobvious to a person skilled in the art). The image capturing units (102,103) may be a camera. A person skilled in art may appreciate that otherkinds of image capturing unit may be used (e.g., thermal cameras, IRcameras, etc). The image capturing units (102, 103) may be configured tocapture images of respective views as represented by dotted lines in theFIG. 1A.

The ECU (104) may receive the images from the first image capturing unit(102) and the second image capturing unit (103) and determine amisalignment between the images. When the images are aligned, the imagesare stitched together. When the images are misaligned, an amount ofmisalignment is determined and the at least one of the first imagecapturing unit (102) and the second image capturing unit (103) areadjusted such that the images captured by the first and the second imagecapturing units (102, 103) are aligned. Finally, the ECU (104) maystitch the aligned images.

As shown in FIG. 1B, the first image capturing unit (102) may capture afirst image (105) comprising a first portion of the scene (100). Thefirst portion of the scene comprises a portion of the tree (108), and aportion of the car (110). The second image capturing unit (103) maycapture a second image (106) comprising a second portion of the scene(100). The second portion of the scene comprises a portion of thebuilding (109) and the car (110). The first image capturing unit (102)may capture the first portion of the scene (100) based on a field ofview of the first image capturing unit (102). The second image capturingunit (103) may capture the second portion of the scene (100) based on afield of view of the second image capturing unit (103). In anembodiment, a predefined amount of overlap (107) may be present betweenthe first image (105) and the second image (106). The predeterminedportion of the overlap (107) may be used to determine an alignmentbetween the first image (105) and the second image (106). As shown inFIG. 1B, the predetermined portion of the overlap may comprise the car(110)

FIG. 2 illustrates internal architecture of the ECU (104) in accordancewith some embodiments of the present disclosure. The ECU (104) mayinclude at least one Central Processing Unit (“CPU” or “processor”)(203) and a memory (202) storing instructions executable by the at leastone processor (203). The processor (203) may comprise at least one dataprocessor for executing program components for executing user orsystem-generated requests. The memory (202) is communicatively coupledto the processor (203). The ECU (104) further comprises an Input/Output(I/O) interface (201). The I/O interface (201) is coupled with theprocessor (203) through which an input signal or/and an output signal iscommunicated.

In an embodiment, data (204) may be stored within the memory (202). Thedata (204) may include, for example, input data (205), segmentation data(206), cluster data (207), confidence score computation data (208),alignment data (209) and other data (210).

In an embodiment, the input data (205) may comprise the first image(105) and the second image (106).

In an embodiment, the segmentation data (206) may comprise data relatedto segmentation of the first image (105) and the second image (106). Thesegmentation data (206) may comprise one or more characteristics of aplurality of pixels of the first image (105) and the second image (106).The one or more characteristics of the plurality of pixels may compriseat least one of, a gray scale level of a plurality of pixels, a powerspectrum of the first image (105) and the second image (106), a textureof the objects in the first image (105) and the second image (106), ashape of objects in the first image (105) and the second image (106), anintensity of the plurality of pixels, and a spatial location of theobjects, and a color of the plurality of pixels.

In an embodiment, the cluster data (207) may comprise data related toformation of the one or more clusters in the first image (105) and thesecond image (106). The cluster data (207) may comprise the one or morecharacteristics of the pixels of the first image (105) and the secondimage (106) required to identify one or more groups of pixels havingsimilar characteristics from the plurality of pixels in each of thefirst image (105) and the second image (106). The cluster data (207) maycomprise the similar characteristics in the plurality of pixels, a classof objects in the first image (105) and the second image (106) and arelative distance between the one or more pixels.

In an embodiment, the confidence score computation data (208) maycomprise data related to the centroids in the one or more clusters and adistance of a pair of centroids from respective centroids in the firstimage (105) and the second image (106). The confidence score computationdata (208) may further comprise the confidence score of the first image(105) and the second image (106). Further, the confidence scorecomputation data (208) may comprise a difference in the confidence scoreof the first image (105) and the second image (106).

In an embodiment, the alignment data (209) may comprise data related tothe alignment of the first image capturing unit (102) and the secondimage capturing unit (103). The first and the second images (105 and106) may be aligned by adjusting at least one of, the first imagecapturing unit (102) and the second image capturing unit (103) based onthe difference in the computation score. The misalignment between thefirst and the second images (105 and 106) may be transformed into amovement required in the first and the second image capturing units (102and 103). For example, an amount of misalignment of the first image(105) with respect to the second image (106) may be 1 cm in a xdirection, considering an x-y plane. A corresponding movement of thefirst image capturing unit (102) may be 2 radians in an x rotationalplane considering a x-y rotational plane. The alignment data (209) maybe data related to the transformation of the misalignments into themovement in the first and the second image capturing units (102 and103).

In an embodiment, the other data (210) may comprise data related to thestitching of the images captured by the vehicle (101). The data relatedto the stitching of the images may be the plurality of pixels of thefirst image (105) and the plurality of the pixels of the second image(106) in the predetermined portion of overlap (107). The data related tothe stitching may comprise matrices representing the first image (105)and the second image (106). Further, the data may include, matrix valuesrequired for stitching.

In an embodiment, the data (204) in the memory (202) may be processed bymodules (211) of the ECU (104). As used herein, the term modules (211)refers to an Application Specific Integrated Circuit (ASIC), anelectronic circuit, a Field-Programmable Gate Arrays (FPGA),Programmable System-on-Chip (PSoC), a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. The modules (211) when configured with the functionalitydefined in the present disclosure will result in a novel hardware.

In one implementation, the modules (211) may include, for example, aninput module (212), a segmentation module (213), a clustering module(214), a confidence score computation module (215), the decision engine(216), the orientation module (217), and other modules (218). It will beappreciated that such aforementioned modules (211) may be represented asa single module or a combination of different modules.

In an embodiment, the input module (212) may receive the first image(105) and the second image (106). The input module (212) may receive thefirst image (105) and the second image (106) from the first imagecapturing unit (102) and the second image capturing unit (103)respectively. In an embodiment, the first image (105) and the secondimage (106) are received in real-time. The input module (212) maypre-process the first and the second images (105 and 106).Pre-processing may include, but not limited to, removing noise,normalizing images, converting the images into matrices, and the like.

In an embodiment, the segmentation module (213) may segment the firstimage (105) and the second image (106) based on the one or morecharacteristics of the plurality of pixels. The segmentation module may,for example, segment the first image (105) and the second image (106)based on the edges in the first image (105) and the second image (106).For example, an image may be having three objects. The three objects maybe segmented based on edge detection techniques. The edge detectiontechniques may comprise finding gradient of pixels in the image todetect the edges and thereby detect the object in the image. In anembodiment, the segmentation may be a semantic segmentation where theobjects are detected based on a label assigned to each pixel of theimage. The pixels having the same labels are grouped, thereby segmentingthe image. Further, the pixels having the same labels may becolor-coded. In an embodiment, artificial neural networks may be usedfor classification based on the labels. A person skilled in art willappreciate that any segmentation techniques can be used to performsegmentation.

In an embodiment, the clustering module (214) may identify the one ormore groups of pixels from the plurality of pixels in each of the firstimage (105) and the second image (106) having similar characteristicsfrom the one or more characteristics. The one or more clusters areidentified in the predetermined portion of overlap (107). For example, atraffic pole in an image may have certain characteristics. The pixels ofthe traffic pole may be very close to each other compared to a distanceof the pixels of the traffic pole from pixels of other objects in theimage. By use of distancing, a group of pixels are formed and the groupof pixels of a first object is differentiated from a second object. Inanother example, the traffic pole may be clustered based on the labelsof individual pixels. The clustering module (214) may determine acentroid for each of the one or more clusters. For example, a K-meansclustering technique may be used. A person skilled in art willappreciate that any clustering techniques can be used.

In an embodiment, the confidence score computation module (215) maycompute the confidence score of the first image (105) and the secondimage (106) based on the centroids of the one or more clusters. Theconfidence score of the first image (105) and the second image (106) maybe output of the Bayesian conditional probability. The Bayesianconditional probability may be a probabilistic distance of each centroidfrom other centroids in respective images (105, 106). The distance froma first centroid to a second centroid may be determined given that athird centroid is present in an image. Further, the confidence scorecomputation unit (215) may compute a difference in the confidence scoreof the first image (105) and the second image (106).

In an embodiment, the decision engine (216) may determine the alignmentof at least one of, the first image capturing unit (102) and the secondimage capturing unit (103). The decision engine (216) may receive thedifference in the confidence score of the first image (105) and thesecond image (106) from the confidence score computation unit (215). Thedecision engine (216) may determine if the difference in the confidencescore is less than a predetermined threshold. When the first image (105)and the second image (106) are misaligned, the distance of the centroidsfrom other centroids in the first image (105) and the distance of thecentroids from other centroids in the second image (106) varies inrespective images, since the relative position of centroids may change.The confidence score which is the output of Bayesian conditionalprobability may reduce for the image which is misaligned. Hence, thedifference in the confidence score of the first image (105) and thesecond image (106) may increase, indicating a reduction in overlapregion. When the difference in the confidence score is greater than thepredetermined threshold, the decision engine (216) may communicate thealignment data (209) to the orientation module (217) to align at leastone of, the first image capturing unit (102) and the second imagecapturing unit (103).

In an embodiment, the orientation module (217) may receive the alignmentdata (209) from the decision engine (216). The orientation module (217)may align at least one of, the first image capturing unit (102) and thesecond image capturing unit (103) when the difference in the confidencescore is greater than the predetermined threshold. The orientationmodule (217) may align at least one of, the first image capturing unit(102) and the second image capturing unit (103) based on the amount ofmisalignment between the first and the second images (105 and 106). Theorientation module (217) may align the first image capturing unit (102)to capture the aligned first image with respect to the second image(106). The orientation module (217) may align the second image capturingunit (103) to capture the aligned second image with respect to the firstimage (105). The orientation module (217) may align both the first imagecapturing unit (102) and the second image capturing unit (103) to reducethe difference in the confidence score.

In an embodiment, the other modules (218) may comprise a stitchingmodule. The stitching module may stitch the images captured by thevehicle (101). The stitching module may perform matrix addition byadding the plurality of pixels of the second image (106) to theplurality of the pixels of the first image (105). A person skilled inart will appreciate that stitching techniques other than matrix additioncan also be used.

FIG. 3 shows a flow chart illustrating a method to stitch the imagescaptured by the vehicle (101), in accordance with some embodiments ofthe present disclosure. As illustrated in FIG. 3, the method (300) maycomprise one or more steps. The method (300) may be described in thegeneral context of computer executable instructions. Generally, computerexecutable instructions can include routines, programs, objects,components, data structures, procedures, modules, and functions, whichperform particular functions or implement particular abstract datatypes.

The order in which the method (300) is described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the method. Additionally,individual blocks may be deleted from the methods without departing fromthe scope of the subject matter described herein. Furthermore, themethod can be implemented in any suitable hardware, software, firmware,or combination thereof.

At step (301), receiving, by the ECU (104), the first image (105)comprising the first portion of the scene (100), from the first imagecapturing unit (102) installed in the vehicle (101) and the second image(106) comprising the second portion of the scene (100), from the secondimage capturing unit (103) installed in the vehicle (101). The firstimage capturing unit (102) may capture the first portion of the scene(100) based on the first field of view of the first image capturing unit(102). The second image capturing unit (103) may capture the secondportion of the scene (100) based on the second field of view of thesecond image capturing unit (103). The first image (105) and the secondimage (106) may have the predetermined portion of overlap (107).Referring to example of FIG. 4A, the ECU (104) receives a first image(401) comprising a first portion of a scene and a second image (402)comprising a second image (402) of the scene.

Referring back to FIG. 3, at step (302), segmenting, by the ECU (104),the first image (105) and the second image (106) based on the one ormore characteristics of the plurality of pixels. The ECU (104) maysegment the first image (105) and the second image (106) based on theone or more characteristics of the pixels of the first image (105) andthe second image (106). The one or more characteristics may comprise thegray scale level of a plurality of pixels. For example, the segmentationmay be performed using conventional edge detection techniques. Objectsin the image may be identified once the edges are identified. Referringto FIG. 4B, the ECU (104) may segment the first image (401) and thesecond image (402) using semantic segmentation to provide segmentedfirst image (403) and segmented second image (404). A person skilled inart will appreciate that any segmentation techniques can be used toperform the segmentation of an image. In the semantic segmentation eachpixel in an image is assigned a class label. The present disclosure hasbeen described considering the color segmentation. The pixels may becolor coded based on different classes. In the segmented first image(403) and the segmented second image (404), building is color coded ingray color, four wheel vehicles are color coded in blue color, road iscolor coded in purple color, people are color coded in red color, streetis color coded in pink color, and signals are coded in yellow color.

Referring back to FIG. 3, at step (303), identifying, by the ECU (104),the one or more groups of pixels from the plurality of pixels in each ofthe first image (105) and the second image (106) having similarcharacteristics from the one or more characteristics. The identified oneor more groups of pixels may form the one or more clusters and acentroid may be determined for each of the one or more clusters in thepredetermined portion of overlap (107). The ECU (104) may identify theone or more groups of the pixels in the first image (105) and the secondimage (106) having the similar characteristics to form the one or moreclusters in the predetermined portion of overlap (107). In anembodiment, the predetermined portion of overlap (107) may be at most10% of an overlapping end of the first image (105) and at most 10% ofthe overlapping end of the second image (106). The one or more clustersmay be formed based on at least one of, the similar characteristics inthe plurality of pixels, a class of objects in the first image (105) andthe second image (106) and a relative distance between the one or morepixels. For example, pixels of a first object in an image may be closeto each other compared to other pixels in the image. A centroid may beassigned to each of the one or more clusters. For example, considerthere are three clusters in the predetermined portion of overlap (107).The K-means clustering technique may use several iterations to determinethe centroids. In a first iteration, a distance between a pixel and thecentroid may be determined randomly. In further iterations, the distanceis minimised by identifying shortest distance between the pixel and thecentroid. A person skilled in the art will appreciate that anyclustering techniques may be used. Referring to the FIG. 4B, thecentroids are determined in the predetermined portion of overlap (405).C1 is the centroid of the building (gray segmented color) in thesegmented first image (403). C2 is the centroid of a car (blue segmentedcolor) in the segmented first image (403). C3 is the centroid of theroad (purple segmented color) in the segmented first image (403). C1′ isthe centroid of the building (gray segmented color) in the segmentedsecond image (404). C2′ is the centroid of the car (blue segmentedcolor) in the segmented second image (404). C3′ is the centroid of theroad (purple segmented color) segmented second image (404).

Referring back to FIG. 3, at step (304), generating, by the ECU (104),the confidence score for the first image (105) and the second image(106) based on the centroid of each of the one or more clusters. Theconfidence score of the first image (105) The confidence score of thefirst image (105) and the second image (106) may be the output of theBayesian conditional probability. The Bayesian conditional probabilitymay be a probabilistic distance of each centroid from respectivecentroids in the second image (105). For example, the Bayesianconditional probability may be a probability of a distance between afirst centroid and a second centroid given third centroid is present inthe image. The confidence score is generated based on an equation (1)

${Confidence}\mspace{14mu}{score}\mspace{14mu}{of}\mspace{14mu}{each}\mspace{14mu}{image}{= {\frac{\sum{{Bayesian}\mspace{14mu}{Conditional}\mspace{14mu}{Probability}}}{{Total}\mspace{14mu}{number}\mspace{14mu}{of}\mspace{14mu}{centroids}} = \frac{{P( {d\; 12} \middle| {C\; 3} )} + {P( {d\; 23} \middle| {C\; 1} )} + {P( {d\; 31} \middle| {C\; 2} )}}{3}}}$

where, P(d12|C3) represents the distance between centroid 1 and centroid2, given centroid 3 present in the image,

P(d23|C1) represents the distance between centroid 2 and centroid 3,given centroid 1 present in the image, andP(d31|C2) represents the distance between centroid 3 and centroid 1,given centroid 2 present in the image.Here, the equation (1) is given considering three centroids. Theequation (1) may be extended to N centroids. Referring to the FIG. 3B,the confidence score for the segmented first image (403) may begenerated as (0.82+0.75+0.90)/3=0.82 (for example). The confidence scorefor the segmented second image (404) may be generated as(0.82+0.78+0.90)/3=0.83 (for example).

Referring back to FIG. 3, at step (305), computing, by the ECU (104),the difference in the confidence score of the first image (105) and thesecond image (106). The ECU (104) may determine the difference in theconfidence score to determine the alignment of the first image (105) andthe second image (106). When the first image (105) and the second image(106) are misaligned, the distance of the centroids from other centroidsin the first image (105) and the distance of the centroids from othercentroids in the second image (106) varies in respective images, sincethe relative position of centroids may change. The confidence scorewhich is the output of Bayesian conditional probability may reduce forthe image which is misaligned. Hence, the difference in the confidencescore of the first image (105) and the second image (106) may increase,indicating a reduction in overlap region. Referring to the FIG. 4B andreferring to paragraph 42, the confidence score of the segmented firstimage (403) may be 0.82 and the confidence score of the segmented secondimage (404) may be 0.83. The difference in the confidence score is 0.01.The difference in the confidence score may be computed by the ECU (104)to determine the alignment of the segmented first image (403) and thesegmented second image (404).

Referring back to FIG. 3, at step (306), aligning, by the ECU (104), atleast one of, the first image capturing unit (102) and the second imagecapturing unit (103) based on the difference in the confidence score,wherein at least one of, the aligned first image capturing unit (102)and the aligned second image capturing unit (103) captures at least oneof, a first aligned image and a second aligned image respectively. TheECU (104) may align at least one of, the first image capturing unit(102) and the second image capturing unit (103) when the difference inthe confidence score is greater than the predetermined threshold. Thecamera ECU (104) may align the first image capturing unit (102) tocapture the aligned first image with respect to the second image (106).The ECU (104) may align the second image capturing unit (103) to capturethe aligned second image with respect to the first image (105). The ECU(104) may align both the first image capturing unit (102) and the secondimage capturing unit (103) to reduce the difference in the confidencescore. The misalignment between the first and the second images (105 and106) may be transformed into a movement required in the first and thesecond image capturing units (102 and 103). For example, an amount ofmisalignment of the first image (105) with respect to the second image(106) may be 1 cm in a x direction, considering an x-y plane. Acorresponding movement of the first image capturing unit (102) may be 2radians in an x rotational plane considering a x-y rotational plane. TheECU (104) may align the first image capturing unit (102) based on theamount of misalignment. Referring to paragraph 43, the difference in theconfidence score is 0.01. The predetermined threshold may be 0.25. Thedifference in the confidence score is less than the predeterminedthreshold. Hence, the alignment of the first image capturing unit (102)and the second image capturing unit (103) may not be needed. FIG. 4Cshows a scenario where the segmented first image (403) and the segmentedsecond image (404) are aligned. FIG. 4D shows a scenario where thesegmented first image (403) and the segmented second image (404) aremisaligned. The difference in the confidence score may be high, when thesegmented first image (403) and the segmented second image (404) aremisaligned. For example, a focus of the second image capturing unit(103) may be different from a focus of the first image capturing unit(102). A relative position of the centroid of the road with respect toother centroids in the segmented second image (404) may be differentfrom the position of the centroid of the road with respect to othercentroids in the segmented first image (403). Hence, the difference inthe confidence score may increase, indicating the misalignment. The ECU(104) may align the second image capturing unit (103) to capture thesecond aligned image until the segmented first image (403) and thesecond aligned image are in a same horizontal level.

Referring back to FIG. 3, at step (307), stitching, by the ECU (104),the first aligned image and the second aligned image. The ECU (104) maystitch the first image (105) and the second image (106) once the firstimage (105) and the second image (106) are aligned. The ECU (104) mayperform matrix addition by adding the plurality of pixels of the secondimage (106) to the plurality of the pixels of the first image (105). Aperson skilled in art will appreciate that stitching techniques otherthan matrix addition can also be used. FIG. 4E shows stitched image. Thesegmented first image (403) and the segmented second image (404) arestitched to form the stitched image.

Computer System

FIG. 5 illustrates a block diagram of an exemplary computer system (500)for implementing embodiments consistent with the present disclosure. Inan embodiment, the computer system (500) is used stitching imagescaptured by a vehicle. The computer system (500) may comprise a centralprocessing unit (“CPU” or “processor”) (502). The processor (502) maycomprise at least one data processor. The processor (502) may includespecialized processing units such as integrated system (bus)controllers, memory management control units, floating point units,graphics processing units, digital signal processing units, etc.

The processor (502) may be disposed in communication with one or moreinput/output (I/O) devices (not shown) via I/O interface (501). The I/Ointerface (501) may employ communication protocols/methods such as,without limitation, audio, analog, digital, monoaural, RCA, stereo,IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC,coaxial, component, composite, digital visual interface (DVI),high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA,IEEE 802.n/b/g/n/x, Bluetooth, cellular (e.g., code-division multipleaccess (CDMA), high-speed packet access (HSPA+), global system formobile communications (GSM), long-term evolution (LTE), WiMax, or thelike), etc.

Using the I/O interface (501), the computer system (500) may communicatewith one or more I/O devices. For example, the input device (510) may bean antenna, keyboard, mouse, joystick, (infrared) remote control,camera, card reader, fax machine, dongle, biometric reader, microphone,touch screen, touchpad, trackball, stylus, scanner, storage device,transceiver, video device/source, etc. The output device (511) may be aprinter, fax machine, video display (e.g., cathode ray tube (CRT),liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasmadisplay panel (PDP), Organic light-emitting diode display (OLED) or thelike), audio speaker, etc.

The processor (502) may be disposed in communication with thecommunication network (509) via a network interface (503). The networkinterface (503) may communicate with the communication network (509).The network interface (503) may employ connection protocols including,without limitation, direct connect, Ethernet (e.g., twisted pair10/100/1000 Base T), transmission control protocol/internet protocol(TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communicationnetwork (509) may include, without limitation, a direct interconnection,local area network (LAN), wide area network (WAN), wireless network(e.g., using Wireless Application Protocol), the Internet, etc. Thenetwork interface (503) may employ connection protocols include, but notlimited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000Base T), transmission control protocol/internet protocol (TCP/IP), tokenring, IEEE 802.11a/b/g/n/x, etc.

The communication network (509) includes, but is not limited to, adirect interconnection, an e-commerce network, a peer to peer (P2P)network, local area network (LAN), wide area network (WAN), wirelessnetwork (e.g., using Wireless Application Protocol), the Internet, Wi-Fiand such. The first network and the second network may either be adedicated network or a shared network, which represents an associationof the different types of networks that use a variety of protocols, forexample, Hypertext Transfer Protocol (HTTP), Transmission ControlProtocol/Internet Protocol (TCP/IP), Wireless Application Protocol(WAP), etc., to communicate with each other. Further, the first networkand the second network may include a variety of network devices,including routers, bridges, servers, computing devices, storage devices,etc.

In some embodiments, the processor (502) may be disposed incommunication with a memory (505) (e.g., RAM, ROM, etc. not shown inFIG. 5) via a storage interface (504). The storage interface (504) mayconnect to memory (505) including, without limitation, memory drives,removable disc drives, etc., employing connection protocols such asserial advanced technology attachment (SATA), Integrated DriveElectronics (IDE), IEEE-1394, Universal Serial Bus (USB), fiber channel,Small Computer Systems Interface (SCSI), etc. The memory drives mayfurther include a drum, magnetic disc drive, magneto-optical drive,optical drive, Redundant Array of Independent Discs (RAID), solid-statememory devices, solid-state drives, etc.

The memory (505) may store a collection of program or databasecomponents, including, without limitation, user interface (506), anoperating system (507), web server (508) etc. In some embodiments,computer system (500) may store user/application data, such as, thedata, variables, records, etc., as described in this disclosure. Suchdatabases may be implemented as fault-tolerant, relational, scalable,secure databases such as Oracle® or Sybase®.

The operating system (507) may facilitate resource management andoperation of the computer system (500). Examples of operating systemsinclude, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-likesystem distributions (E.G., BERKELEY SOFTWARE DISTRIBUTION™ (BSD),FREEBSD™, NETBSD™, OPENBSD™, etc.), LINUX DISTRIBUTIONS™ (E.G., REDHAT™, UBUNTU™, KUBUNTU™, etc.), IBM™ OS/2, MICROSOFT™ WINDOWS™ (XP™,VISTA™/7/8, 10 etc.), APPLE® IOS™, GOOGLE® ANDROID™, BLACKBERRY® OS, orthe like.

In some embodiments, the computer system (500) may implement a webbrowser (508) stored program component. The web browser (508) may be ahypertext viewing application, for example MICROSOFT® INTERNETEXPLORER™, GOOGLE® CHROME™°, MOZILLA® FIREFOX™, APPLE® SAFARI™, etc.Secure web browsing may be provided using Secure Hypertext TransportProtocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security(TLS), etc. Web browsers (508) may utilize facilities such as AJAX™,DHTML™, ADOBE® FLASH™, JAVASCRIPT™, JAVA™, Application ProgrammingInterfaces (APIs), etc. In some embodiments, the computer system (500)may implement a mail server (not shown in Figure) stored programcomponent. The mail server may be an Internet mail server such asMicrosoft Exchange, or the like. The mail server may utilize facilitiessuch as ASP™, ACTIVEX™, ANSI™ C++/C#, MICROSOFT®, .NET™, CGI SCRIPTS™,JAVA™, JAVASCRIPT™, PERL™ PHP™, PYTHON™, WEBOBJECTS™, etc. The mailserver may utilize communication protocols such as Internet MessageAccess Protocol (IMAP), Messaging Application Programming Interface(MAPI), MICROSOFT® exchange, Post Office Protocol (POP), Simple MailTransfer Protocol (SMTP), or the like. In some embodiments, the computersystem (500) may implement a mail client stored program component. Themail client (not shown in Figure) may be a mail viewing application,such as APPLE® MAIL™, MICROSOFT® ENTOURAGE™, MICROSOFT® OUTLOOK™,MOZILLA® THUNDERBIRD™, etc.

In traditional systems, method for stitching images is not efficient asthere are multiple cameras used to obtain a 360° field of view and thereare multiple overlaps within images. The present disclosure forstitching the images tries to overcome the technical problem of thetraditional systems. The present disclosure provides a method forstitching the images with a minimal predetermined portion of overlap.Further, the present disclosure provides a method to stitch the imagesonly after ensuring the images are aligned. Further number of imagecapturing units is reduced in comparison with the traditional systemswhich needed more image capturing units due to high amount of overlap.The present disclosure helps in reducing the space and the cost ininstalling the image capturing units.

In light of the above mentioned advantages and the technicaladvancements provided by the disclosed method and system, the claimedsteps as discussed above are not routine, conventional, or wellunderstood in the art, as the claimed steps enable the followingsolutions to the existing problems in conventional technologies.Further, the claimed steps clearly bring an improvement in thefunctioning of the device itself as the claimed steps provide atechnical solution to a technical problem.

Furthermore, one or more computer-readable storage media may be utilizedin implementing embodiments consistent with the present disclosure. Acomputer-readable storage medium refers to any type of physical memoryon which information or data readable by a processor may be stored.Thus, a computer-readable storage medium may store instructions forexecution by one or more processors, including instructions for causingthe processor(s) to perform steps or stages consistent with theembodiments described herein. The term “computer-readable medium” shouldbe understood to include tangible items and exclude carrier waves andtransient signals, i.e., be non-transitory. Examples include RandomAccess Memory (RAM), Read-Only Memory (ROM), volatile memory,non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks,and any other known physical storage media.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the invention(s)” unless expressly specified otherwise.

The terms “including”, “comprising”, “having” and variations thereofmean “including but not limited to”, unless expressly specifiedotherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise. Theterms “a”, “an” and “the” mean “one or more”, unless expressly specifiedotherwise.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the invention neednot include the device itself.

The illustrated operations of FIG. 3 shows certain events occurring in acertain order. In alternative embodiments, certain operations may beperformed in a different order, modified or removed. Moreover, steps maybe added to the above described logic and still conform to the describedembodiments. Further, operations described herein may occur sequentiallyor certain operations may be processed in parallel. Yet further,operations may be performed by a single processing unit or bydistributed processing units.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based here on. Accordingly, the disclosure of theembodiments of the invention is intended to be illustrative, but notlimiting, of the scope of the invention, which is set forth in thefollowing claims.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A method of stitching images captured by avehicle (101), comprising: receiving, by an Electronic Control Unit(ECU) (104) of the vehicle (101), a first image (105) comprising a firstportion of a scene (100), from a first image capturing unit (102)installed in the vehicle (101) and a second image (106) comprising asecond portion of the scene (100), from a second image capturing unit(103) installed in the vehicle (101); segmenting, by the ECU (104), thefirst image (105) and the second image (106) based on one or morecharacteristics of a plurality of pixels of the first image (105) andthe second image (106); identifying, by the ECU (104), one or moregroups of pixels from the plurality of pixels in each of the first image(105) and the second image (106) having similar characteristics from theone or more characteristics, wherein the identified one or more groupsof pixels form one or more clusters, wherein a centroid is determinedfor each of the one or more clusters in a predetermined portion ofoverlap; generating, by the ECU (104), a confidence score for the firstimage (105) and the second image (106) based on the centroid of each ofthe one or more clusters; computing, by the ECU (104), a difference inthe confidence score of the first image (105) and the second image(106); aligning, by the ECU (104), at least one of, the first imagecapturing unit (102) and the second image capturing unit (103) based onthe difference in the confidence score, wherein at least one of, thealigned first image capturing unit (102) and the aligned second imagecapturing unit (103) captures at least one of, a first aligned image anda second aligned image respectively; and stitching, by the ECU (104),the first aligned image and the second aligned image.
 2. The method ofclaim 1, wherein the first image (105) and the second image (106) havethe predetermined portion of overlap.
 3. The method of claim 1, whereinthe segmentation of the first image (105) and the second image (106) isperformed using Neural Networks.
 4. The method of claim 1, wherein theone or more characteristics comprises at least one of, a gray scalelevel of a plurality of pixels, a power spectrum of the first image(105) and the second image (106), a texture of the objects in the firstimage (105) and the second image (106), a shape of objects in the firstimage (105) and the second image (106), an intensity of the plurality ofpixels, and a spatial location of the objects, and a color of theplurality of pixels.
 5. The method of claim 1, wherein the one or moreclusters are formed based on at least one of, the similarcharacteristics in the plurality of pixels, a class of objects in thefirst image (105) and the second image (106) and a relative distancebetween the one or more pixels.
 6. The method of claim 1, wherein theconfidence score is determined using Bayesian Conditional Probability.7. The method of claim 1, wherein aligning the first image capturingunit (102) and the second image capturing unit (103) comprises:determining, a pair of centroids in each of the first image (105) andthe second image (106); determining, a distance of the pair of centroidsfrom respective centroids in the first image (105) and the second image(106) to determine a misalignment between the first image (105) and thesecond image (106); and aligning at least one of, the first imagecapturing unit (102) and the second image capturing unit (103) based onthe misalignment.
 8. The method of claim 1, wherein stitching the firstaligned image and the second aligned image comprises adding theplurality of pixels of the second aligned image to the plurality ofpixels of the first aligned image along an overlapping end of respectiveimages.
 9. An Electronic Control Unit (ECU) (104) of a vehicle (101),for stitching images captured by a vehicle (101), comprising: one ormore processors (203); and a memory (202), wherein the memory (202)stores processor-executable instructions, which, on execution, cause theprocessor to: receive a first image (105) comprising a first portion ofa scene (100), from a first image capturing unit (102) installed in thevehicle (101) and a second image (106) comprising a second portion ofthe scene (100), from a second image capturing unit (103) installed inthe vehicle (101); segment the first image (105) and the second image(106) based on one or more characteristics of a plurality of pixels ofthe first image (105) and the second image (106); identify one or moregroups of pixels from the plurality of pixels in each of the first image(105) and the second image (106) having similar characteristics from theone or more characteristics, wherein the identified one or more groupsof pixels form one or more clusters, wherein a centroid is determinedfor each of the one or more clusters; generate a confidence score forthe first image (105) and the second image (106) based on the centroidof each of the one or more clusters, wherein the confidence score ofeach of the first and the second image (106) indicates a predeterminedportion of overlap of the first image (105) and the second image (106)respectively; compute a difference in the confidence score of the firstimage (105) and the second image (106); align the first image capturingunit (102) and the second image capturing unit (103) based on thedifference in the confidence score, wherein at least one of, the alignedfirst image capturing unit (102) and the aligned second image capturingunit (103) captures at least one of, a first aligned image and a secondaligned image respectively; and stitch the first aligned image and thesecond aligned image.
 10. The ECU (104) of claim 9, wherein the one ormore processors (203) segments the first image (105) and the secondimage (106) using Neural Networks.
 11. The ECU (104) of claim 9, whereinthe one or more processors (203) forms the one or more clusters areformed based on at least one of, the similar characteristics in theplurality of pixels, a class of objects in the first image (105) and thesecond image (106) and a relative distance between the one or morepixels.
 12. The ECU (104) of claim 9, wherein the one or more processors(203) determines the confidence score using Bayesian ConditionalProbability.
 13. The ECU (104) of claim 9, wherein the one or moreprocessors (203) aligns the first image capturing unit (102) and thesecond image capturing unit (103) by: determining, a pair of centroidseach in the first image (105) and the second image (106); determining, adistance of the pair of centroids from respective centroids in the firstimage (105) and the second image (106) to determine a misalignmentbetween the first image (105) and the second image (106); and aligningat least one of, the first image capturing unit (102) and the secondimage capturing unit (103) based on the misalignment.
 14. The ECU (104)of claim 9, wherein the one or more processors (203) stitches the firstaligned image and the second aligned image by adding the plurality ofpixels of the second aligned image to the plurality of pixels of thefirst aligned image along an overlapping end of respective images.
 15. Anon-transitory computer readable medium including instructions storedthereon that when processed by at least one processor, wherein theinstructions cause an Electronic Control Unit (ECU) (104) to, receive afirst image (105) comprising a first portion of a scene (100), from afirst image capturing unit (102) installed in the vehicle (101) and asecond image (106) comprising a second portion of the scene (100), froma second image capturing unit (103) installed in the vehicle (101);segment the first image (105) and the second image (106) based on one ormore characteristics of a plurality of pixels of the first image (105)and the second image (106); identify one or more groups of pixels fromthe plurality of pixels in each of the first image (105) and the secondimage (106) having similar characteristics from the one or morecharacteristics, wherein the identified one or more groups of pixelsform one or more clusters, wherein a centroid is determined for each ofthe one or more clusters; generate a confidence score for the firstimage (105) and the second image (106) based on the centroid of each ofthe one or more clusters, wherein the confidence score of each of thefirst and the second image (106) indicates a predetermined portion ofoverlap of the first image (105) and the second image (106)respectively; compute a difference in the confidence score of the firstimage (105) and the second image (106); align the first image capturingunit (102) and the second image capturing unit (103) based on thedifference in the confidence score, wherein at least one of, the alignedfirst image capturing unit (102) and the aligned second image capturingunit (103) captures at least one of, a first aligned image and a secondaligned image respectively; and stitch the first aligned image and thesecond aligned image.
 16. The medium of claim 15, wherein theinstructions causes the one or more processors (203) segments the firstimage (105) and the second image (106) using Neural Networks.
 17. Themedium of claim 15, wherein the instructions causes the one or moreprocessors (203) forms the one or more clusters are formed based on atleast one of, the similar characteristics in the plurality of pixels, aclass of objects in the first image (105) and the second image (106) anda relative distance between the one or more pixels.
 18. The medium ofclaim 15, wherein the instructions causes the one or more processors(203) determines the confidence score using Bayesian ConditionalProbability.
 19. The medium of claim 15, wherein the instructions causesthe one or more processors (203) aligns the first image capturing unit(102) and the second image capturing unit (103) by: determining, a pairof centroids each in the first image (105) and the second image (106);determining, a distance of the pair of centroids from respectivecentroids in the first image (105) and the second image (106) todetermine a misalignment between the first image (105) and the secondimage (106); and aligning at least one of, the first image capturingunit (102) and the second image capturing unit (103) based on themisalignment.
 20. The medium of claim 15, wherein the instructionscauses the one or more processors (203) stitches the first aligned imageand the second aligned image by adding the plurality of pixels of thesecond aligned image to the plurality of pixels of the first alignedimage along an overlapping end of respective images.